HAR

Human Activity Recognition

EDA

Load the Data

walkingAlex <- read.csv("~/Documents/Master - HealthCareIT/1. Semester/Introduction to ML/HAR_Project/normal activity/walking/Alex/sensor2 (9).csv")

walkingUpstairsAlex <- read.csv("~/Documents/Master - HealthCareIT/1. Semester/Introduction to ML/HAR_Project/normal activity/walking upstairs/Alex/sensor2 (29).csv")

squatsAlex <- read.csv("~/Documents/Master - HealthCareIT/1. Semester/Introduction to ML/HAR_Project/special activity/Squats/Alex/sensor2 (70).csv")

jumpingJacksAlex <- read.csv("~/Documents/Master - HealthCareIT/1. Semester/Introduction to ML/HAR_Project/special activity/JumpingJacks/Alex/sensor2 (51).csv")


head(walkingAlex)

plot(walkingAlex$time, walkingAlex$atotal, type = "l")

plot(walkingUpstairsAlex$time, walkingUpstairsAlex$atotal, type = "l")

plot(squatsAlex$time, squatsAlex$atotal, type = "l")

plot(jumpingJacksAlex$time, jumpingJacksAlex$atotal, type = "l")

NA
NA

library(dplyr)

Attache Paket: ‘dplyr’

Die folgenden Objekte sind maskiert von ‘package:stats’:

    filter, lag

Die folgenden Objekte sind maskiert von ‘package:base’:

    intersect, setdiff, setequal, union

# Change here your working directory
workingDir <- "~/Documents/Master - HealthCareIT/1. Semester/Introduction to ML/"

walkingFilesName <- list.files(paste(workingDir, "HAR_Project/normal activity/walking/allData", sep = ""),full.names=TRUE)

walkingDataFiles <- lapply(walkingFilesName, read.csv)

walkingUpstairsFilesName <- list.files(paste(workingDir, "HAR_Project/normal activity/walking upstairs/allData", sep = ""),full.names=TRUE)

walkingUpstairsDataFiles <- lapply(walkingUpstairsFilesName, read.csv)

squatsFilesName <- list.files(paste(workingDir, "HAR_Project/special activity/Squats/allData", sep = ""),full.names=TRUE)

squatsDataFiles <- lapply(squatsFilesName, read.csv)

jumpingJackFilesName <- list.files(paste(workingDir, "HAR_Project/special activity/JumpingJacks/allData", sep = ""),full.names=TRUE)

jumpingJackDataFiles <- lapply(jumpingJackFilesName, read.csv)

calculateSmaAe <- function(eventDfList, windowsize){
  SMAs <- c()
  AEs <- c() 
  
  for (x in eventDfList) {
  
    currSmaMax <- 0
    # calculating SMA & AE
    for(i in 1:(nrow(x)/windowsize)){

      ax <- 0
      ay <- 0
      az <- 0
      

      for(ac in (1+((i-1)*windowsize)):(windowsize*i)){
        ax <- ax + abs( x$ax[ac]) 
        ay <- ay + abs(x$ay[ac])
        az <- az + abs(x$az[ac])
      }
      sma <- (ax+ay+az)/windowsize
      
      if(sma > currSmaMax){
        currSmaMax <- sma

        # calculating the AE for the max SMA
        
        ax <- 0
        ay <- 0
        az <- 0
        
        for(j in (1+((i-1)*windowsize)):(windowsize*i)){
          ax <- ax + abs( x$ax[j])^2 
          ay <- ay + abs(x$ay[j])^2
          az <- az + abs(x$az[j])^2
        }
        ae <- (ax+ay+az)/3
      }
    }
    
    SMAs <- append(currSmaMax, SMAs)
    AEs <- append(ae, AEs)
    
  }
  
  plot(SMAs, AEs)
  tabl <- data.frame(SMAs, AEs)
  return(tabl)
}

EDA

Normal Walking

library(ggplot2)

windowsize <- 10

head(walkingDataFiles[[1]])

w <- walkingDataFiles[[1]]

atotalMeans <- c()
atotalMedians <- c()
atotalMins <- c()
atotalMaxs <- c()
SMAs <- c()
AEs <- c() 

for (x in walkingDataFiles) {
  atotalMeans <- append(mean(x$atotal), atotalMeans)
  atotalMedians <- append(median(x$atotal), atotalMedians)
  atotalMins <- append(min(x$atotal), atotalMins)
  atotalMaxs <- append(max(x$atotal), atotalMaxs)

}

walkingSmaAeDf <- calculateSmaAe(walkingDataFiles, windowsize)

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[1] 8.949
[1] 8.949
[1] 8.949
[1] 8.949
[1] 8.949
[1] 8.949
[1] 8.949
[1] 8.949
[1] 8.949
[1] 8.949
[1] 8.949
[1] 8.949
[1] 8.949
[1] 8.949
[1] 8.949
[1] 8.949
[1] 8.949
[1] 8.949
[1] 8.949
[1] 8.949
[1] 8.949
[1] 8.949
[1] 8.949
[1] 8.949
[1] 8.949
[1] 8.949
[1] 8.949
[1] 8.949
[1] 8.949
[1] 8.949
[1] 8.949
[1] 8.949
[1] 8.949
[1] 8.949
[1] 8.949
[1] 8.949
[1] 8.949
[1] 8.949
[1] 8.949
[1] 8.949
[1] 8.949
[1] 8.949
[1] 8.949
[1] 8.949
[1] 8.949
[1] 8.949
[1] 8.949
[1] 8.949
[1] 8.949
[1] 8.949
[1] 8.949
[1] 8.949
[1] 8.949
[1] 8.949
[1] 8.949
[1] 8.949
[1] 8.949
[1] 8.949
[1] 8.949
[1] 8.949
[1] 8.949
[1] 8.949
[1] 8.949
[1] 8.949
[1] 8.949
[1] 8.949
[1] 8.949
[1] 8.949
[1] 8.949
[1] 8.949
[1] 8.949
[1] 8.949
[1] 8.949
[1] 8.949
[1] 8.949
[1] 8.949
[1] 8.949
[1] 8.949
[1] 8.949
[1] 8.949
[1] 0
[1] 1.048
[1] 1.048
[1] 1.048
[1] 1.048
[1] 1.048
[1] 1.048
[1] 1.048
[1] 1.048
[1] 1.048
[1] 1.048
[1] 1.048
[1] 1.048
[1] 1.048
[1] 1.048
[1] 1.048
[1] 1.048
[1] 1.048
[1] 1.048
[1] 1.048
[1] 1.048
[1] 1.048
[1] 1.048
[1] 1.048
[1] 1.048
[1] 1.048
[1] 1.048
[1] 1.048
[1] 1.048
[1] 1.048
[1] 1.048
[1] 1.048
[1] 1.048
[1] 1.048
[1] 1.048
[1] 1.048
[1] 1.048
[1] 1.048
[1] 1.048
[1] 1.048
[1] 1.048
[1] 1.048
[1] 1.048
[1] 1.048
[1] 1.048
[1] 1.048
[1] 1.358
[1] 2.192
[1] 4.893
[1] 4.893
[1] 4.893
[1] 4.893
[1] 4.893
[1] 5.759
[1] 5.759
[1] 5.759
[1] 5.759
[1] 5.759
[1] 7.699
[1] 7.699
[1] 7.699
[1] 7.699
[1] 7.699
[1] 7.699
[1] 7.699
[1] 7.699
[1] 7.699
[1] 7.699
[1] 7.699
[1] 7.699
[1] 7.699
[1] 7.699
[1] 7.699
[1] 7.699
[1] 7.699
[1] 7.699
[1] 7.699
[1] 7.699
[1] 7.699
[1] 7.699
[1] 8.232
[1] 8.232
[1] 8.232
[1] 8.232
[1] 8.232
[1] 8.232
[1] 8.232
[1] 8.232
[1] 8.232
[1] 8.232
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[1] 8.232
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[1] 8.232
[1] 8.232
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[1] 8.232
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[1] 8.232
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[1] 8.232
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[1] 8.232
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[1] 8.232
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[1] 8.232
[1] 8.232
[1] 8.232
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[1] 8.232
[1] 8.232
[1] 8.232
[1] 8.232
[1] 8.232
[1] 8.232
[1] 8.232
[1] 8.232
[1] 8.232
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[1] 8.232
[1] 8.232
[1] 8.232
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[1] 8.232
[1] 8.232
[1] 8.232
[1] 8.232
[1] 8.232
[1] 8.232
[1] 8.232
[1] 8.232
[1] 8.232
[1] 8.232
[1] 8.232
[1] 8.232
[1] 8.545
[1] 8.545
[1] 8.545
[1] 8.545
[1] 8.545
[1] 8.545
[1] 8.545
[1] 8.545
[1] 8.545
[1] 8.545
[1] 8.545
[1] 9.451
[1] 9.451
[1] 9.451
[1] 9.451
[1] 9.451
[1] 9.451
[1] 9.451
[1] 9.451
[1] 9.451
[1] 9.451
[1] 9.451
[1] 9.451
[1] 9.451
[1] 9.451
[1] 9.451
[1] 9.451
[1] 9.451
[1] 9.451
[1] 9.451
[1] 9.451
[1] 9.451
[1] 9.451
[1] 9.451
[1] 9.451
[1] 9.451
[1] 9.451
[1] 9.451
[1] 9.451
[1] 9.451
[1] 9.451
[1] 9.451
[1] 9.451
[1] 9.451
[1] 9.451
[1] 9.451
[1] 9.451
[1] 9.451
[1] 9.451
[1] 0
[1] 0.417
[1] 0.763
[1] 1.814
[1] 1.814
[1] 1.82
[1] 1.82
[1] 1.82
[1] 2.277
[1] 2.277
[1] 2.277
[1] 2.353
[1] 2.353
[1] 2.353
[1] 2.353
[1] 2.353
[1] 2.353
[1] 2.353
[1] 2.353
[1] 2.353
[1] 2.353
[1] 2.353
[1] 2.353
[1] 2.353
[1] 2.353
[1] 2.353
[1] 2.353
[1] 2.353
[1] 2.353
[1] 2.353
[1] 2.353
[1] 2.353
[1] 2.353
[1] 2.353
[1] 2.353
[1] 2.353
[1] 2.353
[1] 2.353
[1] 2.353
[1] 2.353
[1] 2.353
[1] 2.353
[1] 2.353
[1] 2.353
[1] 2.353
[1] 2.353
[1] 2.353
[1] 2.353
[1] 2.353
[1] 3.093
[1] 3.373
[1] 3.373
[1] 3.373
[1] 4.164
[1] 4.164
[1] 4.164
[1] 4.164
[1] 4.374
[1] 4.374
[1] 4.534
[1] 4.534
[1] 4.534
[1] 4.534
[1] 4.534
[1] 4.534
[1] 4.534
[1] 4.534
[1] 4.681
[1] 4.852
[1] 4.852
[1] 5.468
[1] 5.468
[1] 5.468
[1] 5.468
[1] 5.468
[1] 5.468
[1] 5.468
[1] 5.468
[1] 5.468
[1] 5.468
[1] 6.2
[1] 6.2
[1] 6.2
[1] 6.2
[1] 6.2
[1] 6.2
[1] 6.2
[1] 6.2
[1] 6.2
[1] 6.2
[1] 6.2
[1] 6.2
[1] 6.2
[1] 6.2
[1] 6.2
[1] 6.2
[1] 6.2
[1] 6.2
[1] 6.2
[1] 6.2
[1] 6.2
[1] 6.2
[1] 6.2
[1] 6.2
[1] 6.2
[1] 6.2
[1] 6.2
[1] 6.2
[1] 6.2
[1] 6.2
[1] 6.2
[1] 6.2
[1] 6.2
[1] 6.2
[1] 6.2
[1] 6.2
[1] 6.2
[1] 6.2
[1] 6.2
[1] 6.2
[1] 6.2
[1] 6.2
[1] 6.2
[1] 6.2
[1] 6.2
[1] 6.2
[1] 6.2
[1] 6.2
[1] 6.2
[1] 6.2
[1] 6.2
[1] 6.2
[1] 6.2
[1] 6.2
[1] 6.2
[1] 6.2
[1] 6.2
[1] 6.2
[1] 6.2
[1] 6.2
[1] 6.2
[1] 6.2
[1] 6.2
[1] 6.2
[1] 6.2
[1] 6.2
[1] 6.2
[1] 6.2
[1] 6.2
[1] 6.2
[1] 6.2
[1] 6.2
[1] 6.2
[1] 6.718
[1] 6.718
[1] 6.718
[1] 6.718
[1] 6.718
[1] 6.718
[1] 6.718
[1] 6.718
[1] 6.718
[1] 6.718
[1] 6.718
[1] 6.718
[1] 6.718
[1] 6.718
[1] 6.718
[1] 6.718
[1] 6.718
[1] 6.718
[1] 6.718
[1] 6.718
[1] 6.718
[1] 6.718
[1] 6.718
[1] 6.718
[1] 6.718
[1] 6.718
[1] 6.718
[1] 6.718
[1] 6.718
[1] 6.718
[1] 6.718
[1] 6.722
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[1] 6.722
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[1] 6.722
[1] 6.722
[1] 6.722
[1] 6.722
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[1] 6.722
[1] 6.722
[1] 6.722
[1] 6.722
[1] 6.722
[1] 6.722
[1] 6.722
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[1] 6.722
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[1] 6.722
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[1] 6.722
[1] 6.722
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[1] 6.722
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[1] 6.722
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[1] 6.722
[1] 6.722
[1] 6.722
[1] 6.722
[1] 6.722
[1] 6.722
[1] 6.722
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[1] 6.722
[1] 6.722
[1] 6.722
[1] 6.722
[1] 6.722
[1] 6.722
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[1] 6.722
[1] 6.722
[1] 6.722
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[1] 6.722
[1] 6.722
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[1] 6.722
[1] 6.722
[1] 6.722
[1] 6.722
[1] 6.722
[1] 6.722
[1] 6.722
[1] 6.722
[1] 6.722
[1] 6.722
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[1] 6.722
[1] 6.722
[1] 6.722
[1] 6.722
[1] 6.722
[1] 6.722
[1] 6.722
[1] 6.722
[1] 6.722
[1] 6.722
[1] 6.722
[1] 6.722
[1] 6.722
[1] 6.722
[1] 6.722
[1] 6.722
[1] 6.722
[1] 6.722
[1] 6.722
[1] 6.722
[1] 6.722
[1] 6.722
[1] 6.722
[1] 6.722
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[1] 6.722
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[1] 6.722
[1] 6.722
[1] 6.722
[1] 6.722
[1] 6.722
[1] 6.722
[1] 6.722
[1] 6.722
[1] 6.722
[1] 6.722
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[1] 7.638
[1] 7.638
[1] 7.638
[1] 7.638
[1] 7.638
[1] 7.638
[1] 7.638
[1] 7.638
[1] 7.638
[1] 7.638
[1] 7.638
[1] 7.638
[1] 7.638
[1] 7.638
[1] 7.638
[1] 0
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[1] 0.376
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[1] 0.376
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[1] 0.376
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[1] 0.376
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[1] 0.376
[1] 0.376
[1] 0.376
[1] 0.376
[1] 0.376
[1] 0.376
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[1] 0.376
[1] 0.376
[1] 0.376
[1] 0.376
[1] 0.376
[1] 0.376
[1] 0.376
[1] 0.376
[1] 0.376
[1] 0.376
[1] 0.376
[1] 0.376
[1] 0.376
[1] 0.376
[1] 0.71
[1] 1.243
[1] 1.797
[1] 1.797
[1] 2.324
[1] 2.324
[1] 2.324
[1] 2.621
[1] 2.621
[1] 2.621
[1] 2.621
[1] 2.621
[1] 2.621
[1] 2.621
[1] 2.621
[1] 2.621
[1] 2.621
[1] 2.621
[1] 2.621
[1] 2.621
[1] 2.621
[1] 2.621
[1] 2.621
[1] 3.205
[1] 3.205
[1] 3.205
[1] 3.205
[1] 3.205
[1] 3.205
[1] 3.205
[1] 3.205
[1] 3.205
[1] 3.205
[1] 3.205
[1] 3.205
[1] 3.205
[1] 3.205
[1] 3.205
[1] 3.205
[1] 3.205
[1] 3.205
[1] 3.205
[1] 3.205
[1] 3.205
[1] 3.205
[1] 3.277
[1] 3.277
[1] 3.277
[1] 4.472
[1] 4.472
[1] 4.472
[1] 4.472
[1] 4.472
[1] 4.472
[1] 5.377
[1] 5.377
[1] 5.377
[1] 5.377
[1] 5.377
[1] 5.377
[1] 5.377
[1] 5.377
[1] 5.377
[1] 5.377
[1] 5.377
[1] 5.964
[1] 5.964
[1] 5.964
[1] 5.964
[1] 5.964
[1] 5.964
[1] 5.964
[1] 5.964
[1] 5.964
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[1] 5.964
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[1] 5.964
[1] 5.964
[1] 5.964
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[1] 5.964
[1] 5.964
[1] 5.964
[1] 5.964
[1] 5.964
[1] 5.964
[1] 5.964
[1] 5.964
[1] 5.964
[1] 5.964
[1] 5.964
[1] 5.964
[1] 5.964
[1] 5.964
[1] 5.964
[1] 5.964
[1] 5.964
[1] 5.964
[1] 5.964
[1] 5.964
[1] 5.964
[1] 5.964
[1] 5.964
[1] 7.772
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[1] 7.772
[1] 7.772
[1] 7.772
[1] 7.772
[1] 7.772
[1] 7.772
[1] 7.772
[1] 7.772
[1] 7.772
[1] 7.772
[1] 7.772
[1] 7.772
[1] 7.772
[1] 7.772
[1] 7.772
[1] 7.772
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[1] 7.772
[1] 7.772
[1] 7.772
[1] 7.772
[1] 7.772
[1] 7.772
[1] 7.772
[1] 7.772
[1] 7.772
[1] 7.772
[1] 7.772
[1] 7.772
[1] 7.772
[1] 7.772
[1] 7.772
[1] 7.772
[1] 7.772
[1] 7.772
[1] 7.772
[1] 7.772
[1] 7.772
[1] 7.772
[1] 7.772
[1] 7.772
[1] 7.772
[1] 7.772
[1] 7.772
[1] 7.772
[1] 7.772
[1] 7.772
[1] 7.772
[1] 7.772
[1] 7.772
[1] 7.772
[1] 7.772
[1] 7.772
[1] 7.772
[1] 7.772
[1] 7.772
[1] 7.772
[1] 7.772
[1] 7.772
[1] 7.772
[1] 7.772
[1] 7.772
[1] 7.772
[1] 7.772
[1] 7.772
[1] 7.772
[1] 7.772
[1] 7.772
[1] 7.772
[1] 7.772
[1] 7.772
[1] 7.772
[1] 7.772
[1] 7.772
[1] 7.772
[1] 7.772
[1] 7.772
[1] 7.772
[1] 7.772
[1] 7.772
[1] 7.772
[1] 7.772
[1] 7.772
[1] 7.772
[1] 7.772
[1] 7.772
[1] 7.772
[1] 7.772
[1] 7.772
[1] 7.772
[1] 7.772
[1] 7.772
[1] 7.772
[1] 7.772
[1] 7.772
[1] 7.772
[1] 7.772
[1] 7.772
[1] 0
[1] 0.543
[1] 0.543
[1] 0.543
[1] 0.543
[1] 0.561
[1] 0.561
[1] 0.561
[1] 0.561
[1] 1.65
[1] 1.65
[1] 1.65
[1] 1.65
[1] 1.65
[1] 1.65
[1] 1.65
[1] 1.65
[1] 1.65
[1] 1.65
[1] 1.65
[1] 7.015
[1] 9.024
[1] 17.939
[1] 17.939
[1] 29.306
[1] 29.306
[1] 29.306
[1] 29.306
[1] 29.306
[1] 29.306
[1] 29.306
[1] 29.306
[1] 29.306
[1] 29.306
[1] 29.306
[1] 29.306
[1] 29.306
[1] 29.306
[1] 29.306
[1] 29.306
[1] 29.306
[1] 29.306
[1] 29.306
[1] 29.306
[1] 29.306
[1] 29.306
[1] 29.306
[1] 29.306
[1] 29.306
[1] 29.306
[1] 29.306
[1] 29.306
[1] 29.306
[1] 29.306
[1] 29.306
[1] 29.306
[1] 29.306
[1] 29.306

walkingSmaAeDf$class <- "walking"

par(mfrow=c(2,2))

boxplot(atotalMeans, main="Mean")
boxplot(atotalMedians, main="Median")

boxplot(atotalMins, main="Minimum")
boxplot(atotalMaxs, main="Maximum")

Walking upstairs

atotalMeans <- c()
atotalMedians <- c()
atotalMins <- c()
atotalMaxs <- c()

for (x in walkingUpstairsDataFiles) {
  
  atotalMeans <- append(mean(x$atotal), atotalMeans)
  atotalMedians <- append(median(x$atotal), atotalMedians)
  atotalMins <- append(min(x$atotal), atotalMins)
  atotalMaxs <- append(max(x$atotal), atotalMaxs)

  
  #lines(x$time, x$atotal, type = "l")
}

walkingUpstairsSmaAeDf <- calculateSmaAe(walkingUpstairsDataFiles, windowsize)
walkingUpstairsSmaAeDf$class <- "walkingUpstairs"

par(mfrow=c(2,2))

boxplot(atotalMeans, main="Mean")
boxplot(atotalMedians, main="Median")

boxplot(atotalMins, main="Minimum")
boxplot(atotalMaxs, main="Maximum")

Jumping Jacks

atotalMeans <- c()
atotalMedians <- c()
atotalMins <- c()
atotalMaxs <- c()
SMAs <- c()

for (x in jumpingJackDataFiles) {
  atotalMeans <- append(mean(x$atotal), atotalMeans)
  atotalMedians <- append(median(x$atotal), atotalMedians)
  atotalMins <- append(min(x$atotal), atotalMins)
  atotalMaxs <- append(max(x$atotal), atotalMaxs)

  ax <- 0
  ay <- 0
  az <- 0
  for(ac in 1:nrow(x)){
    ax <- ax + abs( x$ax[ac]) 
    ay <- ay + abs(x$ay[ac])
    az <- az + abs(x$az[ac])
  }
  
  sma <- (ax+ay+az)/nrow(x)
  SMAs <- append(sma, SMAs)
  
  #lines(x$time, x$atotal, type = "l")
}

jumpingJacksSmaAeDf <-  calculateSmaAe(jumpingJackDataFiles, windowsize)

jumpingJacksSmaAeDf$class <- "jumpingJacks"

plot(SMAs)

par(mfrow=c(2,2))

boxplot(atotalMeans, main="Mean")
boxplot(atotalMedians, main="Median")

boxplot(atotalMins, main="Minimum")
boxplot(atotalMaxs, main="Maximum")

Squats

atotalMeans <- c()
atotalMedians <- c()
atotalMins <- c()
atotalMaxs <- c()

for (x in squatsDataFiles) {
  atotalMeans <- append(mean(x$atotal), atotalMeans)
  atotalMedians <- append(median(x$atotal), atotalMedians)
  atotalMins <- append(min(x$atotal), atotalMins)
  atotalMaxs <- append(max(x$atotal), atotalMaxs)

  
  #lines(x$time, x$atotal, type = "l")
}

squatsSmaAeDf <-  calculateSmaAe(squatsDataFiles, windowsize)
squatsSmaAeDf$class <- "squats"

par(mfrow=c(2,2))

boxplot(atotalMeans, main="Mean")
boxplot(atotalMedians, main="Median")

boxplot(atotalMins, main="Minimum")
boxplot(atotalMaxs, main="Maximum")

SMA/AE Clustering

allSmaAe <- rbind(walkingSmaAeDf, walkingUpstairsSmaAeDf, jumpingJacksSmaAeDf, squatsSmaAeDf)
allSmaAe$class <- as.factor(allSmaAe$class)
boxplot(allSmaAe$AEs)

plot(allSmaAe$SMAs, allSmaAe$AEs,  col = allSmaAe$class)

ggplot(allSmaAe, aes(SMAs, AEs, col=class)) + geom_point()

At this point we can nicely differentiate the jumping jacks with the SMA & AE, but the other ADLs can’t be sperated good enough.

We have to find features for differentiate: squats, walking & walking upstairs.

Let’s look at the frequency analysis of these activities.

library("signal")

fs <- 100
nyq <- fs/2

# Walking
plot(walkingAlex$time, walkingAlex$atotal, type = "l")

fftWalkingAlex <- fft(walkingAlex$atotal)
plot(abs(fftWalkingAlex), type = "l", xlab = "Frequency (Hz)", ylab = "Amplitude")

fir <- fir1(230, c(0.8/nyq, 3.2/nyq), type = "pass")
#bf <- butter(73, c(1/nyq, 4/nyq), type = "pass") 
freqz(fir, Fs = fs)

y <- filtfilt(fir, walkingAlex$atotal)
plot(y, type = "l")

plot(abs(fft(y)), type = "l", xlab = "Frequency (Hz)", ylab = "Amplitude", xlim = c(0,150))

which.max(abs(fft(y))[1:500])

[1] 60

# Walking upstairs
plot(walkingUpstairsAlex$time, walkingUpstairsAlex$atotal, type = "l")

y <- filtfilt(fir, walkingUpstairsAlex$atotal)
plot(y, type = "l")

plot(abs(fft(y)), type = "l", xlab = "Frequency (Hz)", ylab = "Amplitude", xlim = c(0,150))

which.max(abs(fft(y))[1:500])

[1] 44

# squats
plot(squatsAlex$time, squatsAlex$atotal, type = "l")

y <- filtfilt(fir, squatsAlex$atotal)
plot(y, type = "l")

plot(abs(fft(y)), type = "l", xlab = "Frequency (Hz)", ylab = "Amplitude", xlim = c(0,150))

which.max(abs(fft(y))[1:500])

[1] 23

Regarding to the analysis of the different frequency, there could be probably a difference. Frequency with the max. Amplitude: - normal walking: 60 - walking upstairs: 44 - squats: 23

Lets apply this to all of the data.

fs <- 100
nyq <- fs/2

fir <- fir1(230, c(0.8/nyq, 3.2/nyq), type = "pass")

# normal walking

maxFreqWalking <- c()

for (x in walkingDataFiles) {
  y <- filtfilt(fir, x$atotal)
  maxF <- which.max(abs(fft(y))[1:100])
  maxFreqWalking <-  append(maxF, maxFreqWalking)
}

# walking upstairs

maxFreqWalkingUpstairs <- c()

for (x in walkingUpstairsDataFiles) {
  y <- filtfilt(fir, x$atotal)
  maxF <- which.max(abs(fft(y))[1:100])
  maxFreqWalkingUpstairs <-  append(maxF, maxFreqWalkingUpstairs)
}


# squats

maxFreqSquats <- c()

for (x in squatsDataFiles) {
  y <- filtfilt(fir, x$atotal)
  maxF <- which.max(abs(fft(y))[1:100])
  maxFreqSquats <-  append(maxF, maxFreqSquats)
}


par(mfrow=c(1,3))
boxplot(maxFreqWalking, main="walking", ylim = c(10,70))
boxplot(maxFreqWalkingUpstairs, main="walking upstairs", ylim = c(10,70))

boxplot(maxFreqSquats, main="squats", ylim = c(10,70))

Looking at the boxplots, we can see a pretty nice separation from the different activities.